The Need for Social Emotional Learning (SEL) in the Education of Refugee ELLs.
Bibliographic record
Abstract
PURPOSE: The purpose of this project is to explore and collect data to demonstrate areas of need in the modern education system. Specifically, my research will focus on the socioemotional learning scenarios that may be absent in the teaching of refugee ELLs. Lastly, this thesis will seek to provide suggestions about how to use this data to further develop best practices in teaching to better serve all students. SUBJECTS: The subjects in this thesis are refugee English Language Learners (ELLs) in classrooms throughout the state of Michigan; with a specific focus on Ottawa Hills High School in GRPS. METHODS AND MATERIALS: This thesis utilizes data collected through responses on Panorama surveys, WIDA tests, and other state administered surveys to analyze the connection between SEL strategies and student performance and comfort. In addition, data has been collected through the Early Warning Indicator (EWI) Data Dashboard. RESULTS: It is expected the results will show a strong correlation between the implementation of SEL strategies and positive educational experiences for refugee ELLs in the classroom. CONCLUSIONS: Based on the research findings in this thesis project, one will be able to conclude that schools need to work more diligently to implement SEL teaching practices in modern classrooms. These strategies help to create safe learning environments which lead to increased feelings of comfort and safety amongst students. As a result, students, especially refugee ELLs, are more likely to have more positive experiences in the classroom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".